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Amazon Amazon-DEA-C01 Exam Prep
AWS Certified Data Engineer - Associate (Page 2 )

Updated On: 15-Sep-2026
Page 2 of 47
PDF with 366 Questions

A data engineer is configuring an AWS Glue job to read data from an Amazon S3 bucket. The data engineer has set up the necessary AWS Glue connection details and an associated IAM role. However, when the data engineer attempts to run the AWS Glue job, the data engineer receives an error message that indicates that there are problems with the Amazon S3 VPC gateway endpoint. The data engineer must resolve the error and connect the AWS Glue job to the S3 bucket.
Which solution will meet this requirement?

  1. Update the AWS Glue security group to allow inbound traffic from the Amazon S3 VPC gateway endpoint.
  2. Configure an S3 bucket policy to explicitly grant the AWS Glue job permissions to access the S3 bucket.
  3. Review the AWS Glue job code to ensure that the AWS Glue connection details include a fully qualified domain name.
  4. Verify that the VPC's route table includes inbound and outbound routes for the Amazon S3 VPC gateway endpoint.

Answer(s): D

Explanation:

D: Verify that the VPC's route table includes inbound and outbound routes for the Amazon S3 VPC gateway endpoint.
The error specifically mentions problems with the Amazon S3 VPC gateway endpoint.
For a gateway endpoint to work:
The VPC route tables must include routes that direct S3 traffic to the endpoint
Without proper routing, Glue jobs running inside the VPC cannot reach S3
Fixing the route table ensures private connectivity between AWS Glue and S3.



A retail company has a customer data hub in an Amazon S3 bucket. Employees from many countries use the data hub to support company-wide analytics. A governance team must ensure that the company's data analysts can access data only for customers who are within the same country as the analysts.
Which solution will meet these requirements with the LEAST operational effort?

  1. Create a separate table for each country's customer data. Provide access to each analyst based on the country that the analyst serves.
  2. Register the S3 bucket as a data lake location in AWS Lake Formation. Use the Lake Formation row-level security features to enforce the company's access policies.
  3. Move the data to AWS Regions that are close to the countries where the customers are. Provide access to each analyst based on the country that the analyst serves.
  4. Load the data into Amazon Redshift. Create a view for each country. Create separate IAM roles for each country to provide access to data from each country. Assign the appropriate roles to the analysts.

Answer(s): B

Explanation:

The correct answer is B because it provides a centralized and scalable solution for managing data access based on row-level security using AWS Lake Formation. Lake Formation simplifies the process of building, securing, and managing data lakes. Option B leverages Lake Formation's row-level security feature which allows filtering data based on attributes. In this case, it can restrict access to customer data based on the analyst's country, meeting the governance team's requirement of analysts only seeing data for their respective countries.Option A involves creating separate tables per country, leading to data duplication, increased storage costs, and higher operational overhead for managing multiple tables. This approach is less efficient than using row-level security. Option C involves moving data to different AWS Regions, which adds complexity in data management, data transfer costs, and potential latency issues. Option D suggests using Amazon Redshift and creating views.
While viable, it necessitates loading data into Redshift (adding complexity and cost) and managing access via IAM roles. It is also more operationally intensive than Lake Formation's row-level security features, which are specifically designed for this type of access control. Lake Formation provides fine-grained access control without modifying the underlying data storage in S3, minimizing operational effort.
Therefore, using Lake Formation for row-level security is the most efficient and scalable solution for enforcing the required access policies with minimal operational overhead.
Relevant Documentation:
AWS Lake Formation Row-Level Security AWS Lake Formation Fine-Grained Access Control



A media company wants to improve a system that recommends media content to customer based on user behavior and preferences. To improve the recommendation system, the company needs to incorporate insights from third-party datasets into the company's existing analytics platform. The company wants to minimize the effort and time required to incorporate third-party datasets.
Which solution will meet these requirements with the LEAST operational overhead?

  1. Use API calls to access and integrate third-party datasets from AWS Data Exchange.
  2. Use API calls to access and integrate third-party datasets from AWS DataSync.
  3. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from AWS CodeCommit repositories.
  4. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from Amazon Elastic Container Registry (Amazon ECR).

Answer(s): A

Explanation:

A: Use API calls to access and integrate third-party datasets from AWS Data Exchange.
The company needs:
Third-party datasets
Quick integration
Minimal operational effort
AWS Data Exchange is specifically built for:
Discovering and subscribing to third-party datasets
Direct integration with analytics platforms
Accessing data via APIs or direct delivery to services like S3, Redshift, etc.
This provides the fastest and lowest-overhead method to incorporate external data.



A financial company wants to implement a data mesh. The data mesh must support centralized data governance, data analysis, and data access control. The company has decided to use AWS Glue for data catalogs and extract, transform, and load (ETL) operations.
Which combination of AWS services will implement a data mesh? (Choose two.)

  1. Use Amazon Aurora for data storage. Use an Amazon Redshift provisioned cluster for data analysis.
  2. Use Amazon S3 for data storage. Use Amazon Athena for data analysis.
  3. Use AWS Glue DataBrew for centralized data governance and access control.
  4. Use Amazon RDS for data storage. Use Amazon EMR for data analysis.
  5. Use AWS Lake Formation for centralized data governance and access control.

Answer(s): B,E

Explanation:

The correct answer is B E
Here's why:
B: Use Amazon S3 for data storage. Use Amazon Athena for data analysis. Amazon S3: S3 is a highly scalable, durable, and cost-effective object storage service ideal for storing large volumes of structured and unstructured data in a data lake architecture. Data meshes often rely on decentralized data ownership, and S3 allows each domain to store their data independently in separate buckets or prefixes. https://aws.amazon.com/s3/ Amazon Athena: Athena is a serverless query service that allows you to analyze data directly in S3 using standard SQL. This is critical for data analysis in a data mesh, as it allows data consumers to query data from various domains stored in S3 without the need for data warehousing. Its serverless nature reduces operational overhead. https://aws.amazon.com/athena/
E: Use AWS Lake Formation for centralized data governance and access control. AWS Lake Formation: Lake Formation simplifies building, securing, and managing data lakes. A key tenet of a data mesh is federated governance, and Lake Formation facilitates this by providing centralized control over data access, security, and compliance across the data lake. It integrates with AWS Glue for data cataloging and allows you to define fine-grained permissions on data in S3. Centralized governance becomes possible because Lake Formation offers fine grained access control over data stored in S3. https://aws.amazon.com/lake-formation/
Here's why the other options are less suitable:

A: Use Amazon Aurora for data storage. Use an Amazon Redshift provisioned cluster for data analysis. Aurora is suitable for transactional workloads, not generally for storing data in a data lake environment like in S3, although Aurora can be used in conjunction with S3. A Redshift provisioned cluster requires capacity planning and ongoing management, conflicting with the ease of use and scalability desired in a data mesh.
C: Use AWS Glue DataBrew for centralized data governance and access control.
While Glue DataBrew can be used to cleanse and normalize data, it does not provide the centralized data governance and access control capabilities on the scale of a data lake in S3 that Lake Formation does. It's designed more for data preparation, not comprehensive governance.
D: Use Amazon RDS for data storage. Use Amazon EMR for data analysis. RDS is designed for relational databases, and like Aurora, not for storing data lakes as efficiently as S3. EMR is powerful for big data processing but might be overkill and more complex to manage compared to Athena for simpler querying use cases common in a data mesh. EMR focuses on processing, while Athena focuses on querying.



A data engineer maintains custom Python scripts that perform a data formatting process that many AWS Lambda functions use.
When the data engineer needs to modify the Python scripts, the data engineer must manually update all the Lambda functions. The data engineer requires a less manual way to update the Lambda functions.
Which solution will meet this requirement?

  1. Store a pointer to the custom Python scripts in the execution context object in a shared Amazon S3 bucket.
  2. Package the custom Python scripts into Lambda layers. Apply the Lambda layers to the Lambda functions.
  3. Store a pointer to the custom Python scripts in environment variables in a shared Amazon S3 bucket.
  4. Assign the same alias to each Lambda function. Call reach Lambda function by specifying the function's alias.

Answer(s): B

Explanation:

The correct answer is
B: Package the custom Python scripts into Lambda layers. Apply the Lambda layers to the Lambda functions.
Lambda layers offer a way to centrally manage and share code dependencies across multiple Lambda functions. Instead of embedding the custom Python scripts directly within each Lambda function's deployment package, the data engineer can package these scripts as a Lambda layer. Then, each Lambda function can be configured to use this layer.
When the Python scripts need to be updated, the data engineer only needs to update the Lambda layer. All Lambda functions using that layer will automatically receive the updated code upon their next invocation, without requiring individual redeployments. This significantly reduces the manual effort involved in updating multiple functions.
Option A is incorrect because storing pointers to scripts in S3 would require each Lambda function to fetch the script dynamically at runtime. This adds latency, complexity in terms of error handling (S3 unavailability), and requires each function to have the necessary S3 permissions. It also doesn't address dependency management.
Option C is similar to Option A in that it relies on external storage and introduces runtime dependencies. Environment variables are intended for configuration parameters, not code.
Option D, aliases, are primarily for version management and traffic routing, not for sharing code dependencies.
While aliases can point to different versions of a Lambda function, they don't address the underlying problem of code duplication. Each version would still need to contain the shared Python scripts.
Lambda Layers streamline dependency management, reduce deployment package size for individual functions, and simplify updates across numerous functions, making it the best solution.
Refer to the AWS documentation for more details:
AWS Lambda Layers Using Lambda Layers



A company created an extract, transform, and load (ETL) data pipeline in AWS Glue. A data engineer must crawl a table that is in Microsoft SQL Server. The data engineer needs to extract, transform, and load the output of the crawl to an Amazon S3 bucket. The data engineer also must orchestrate the data pipeline.
Which AWS service or feature will meet these requirements MOST cost-effectively?

  1. AWS Step Functions
  2. AWS Glue workflows
  3. AWS Glue Studio
  4. Amazon Managed Workflows for Apache Airflow (Amazon MWAA)

Answer(s): B

Explanation:

Here's a detailed justification for why AWS Glue workflows are the most cost-effective solution:
The scenario requires crawling a SQL Server table, ETL processing using AWS Glue, storing the output in S3, and orchestrating the entire pipeline.
AWS Glue Crawlers: Glue Crawlers are designed to discover the schema of data sources like Microsoft SQL Server and register them in the AWS Glue Data Catalog. This addresses the need to crawl the table and prepare its metadata for ETL. AWS Glue Jobs: Glue jobs are used to perform the ETL operations, extracting data from SQL Server (using the crawler's metadata as a source), transforming it, and loading it into the S3 bucket. AWS Glue Workflows: Glue Workflows are a feature within AWS Glue specifically designed for orchestrating Glue Crawlers and Glue Jobs. They allow you to define dependencies and trigger sequential or parallel execution of these tasks in a managed, serverless environment. Workflows manage dependencies effectively.
Now, let's compare the options:

A: AWS Step Functions: Step Functions can orchestrate various AWS services, including Glue, but it adds an extra layer of complexity and cost. It is better suited for orchestrating more complex, heterogeneous workloads involving multiple AWS services beyond just Glue itself. For a pure Glue-based pipeline, Glue workflows are more efficient.
B: AWS Glue workflows: This option directly addresses the need for orchestration within the AWS Glue ecosystem, managing the crawling and ETL process efficiently. This reduces operational overhead, provides monitoring, and offers a serverless orchestration environment tailored for Glue tasks.
C: AWS Glue Studio: Glue Studio provides a visual interface for building ETL jobs, but it doesn't directly handle the orchestration of the entire pipeline (crawl, ETL, load).
While you can create the ETL job itself in Glue Studio, you still need a separate orchestration tool.
D: Amazon Managed Workflows for Apache Airflow (Amazon MWAA): MWAA is a fully managed Apache Airflow service.
While Airflow is a powerful orchestration tool, it is considerably more complex and expensive than Glue workflows for a simple Glue-centric pipeline. MWAA is generally preferred for organizations already using Airflow or requiring the full flexibility and extensibility of Airflow's ecosystem.
Considering the cost-effectiveness requirement, AWS Glue workflows are the optimal choice because they provide a managed orchestration service that is tightly integrated with AWS Glue Crawlers and Jobs, avoiding the overhead of managing a separate orchestration service like Step Functions or MWAA. Also, orchestration is the native intention of AWS Glue Workflows.
Supporting links:
AWS Glue Workflows: https://docs.aws.amazon.com/glue/latest/dg/workflows-using.html AWS Glue Pricing: https://aws.amazon.com/glue/pricing/ AWS Step Functions Pricing: https://aws.amazon.com/step-functions/pricing/ Amazon MWAA Pricing: https://aws.amazon.com/managed-workflows-for-apache-airflow/pricing/



A financial services company stores financial data in Amazon Redshift. A data engineer wants to run real-time queries on the financial data to support a web-based trading application. The data engineer wants to run the queries from within the trading application.
Which solution will meet these requirements with the LEAST operational overhead?

  1. Establish WebSocket connections to Amazon Redshift.
  2. Use the Amazon Redshift Data API.
  3. Set up Java Database Connectivity (JDBC) connections to Amazon Redshift.
  4. Store frequently accessed data in Amazon S3. Use Amazon S3 Select to run the queries.

Answer(s): B

Explanation:

The correct answer is B, using the Amazon Redshift Data API. Here's why:
Real-time Queries: The scenario necessitates a solution that can execute queries quickly to support the web-based trading application.
Least Operational Overhead: The key is minimizing the burden on the data engineer for managing and maintaining the connection infrastructure.
Redshift Data API: This API provides a serverless, HTTP-based interface to execute SQL commands on Amazon Redshift clusters. It removes the need to manage persistent connections, connection pools, or drivers within the trading application, simplifying development and reducing operational overhead. You simply invoke the API with your SQL statement and retrieve the results.
WebSocket Connections (Option A): While WebSockets can provide real-time communication, managing them directly with Amazon Redshift would require significant custom development to handle authentication, connection management, and error handling. This adds complexity and operational overhead.
JDBC Connections (Option C): JDBC requires the application to maintain connections to the Redshift cluster. This involves managing connection pools, dealing with connection failures, and potentially impacting the Redshift cluster's performance due to the overhead of managing a large number of persistent connections.
While viable, it's more complex to manage than the Data API.
Amazon S3 Select (Option D): S3 Select is suitable for querying data directly within S3 but requires you to first load your Redshift data into S3. This adds an ETL process and is not efficient or appropriate for "real-time" queries on the data already residing in Redshift. It would also be slower compared to querying directly within the Redshift data warehouse.
In summary, the Redshift Data API offers the simplest and most efficient way to run real-time queries from an application on Amazon Redshift with minimal overhead. It provides a serverless, managed interface for executing SQL commands without the complexities of managing persistent connections.
Authoritative Links:
Amazon Redshift Data API: https://docs.aws.amazon.com/redshift/latest/dg/data-api.html AWS Documentation on JDBC connections to Redshift: https://docs.aws.amazon.com/redshift/latest/dg/java-jdbc.html Amazon S3 Select: https://docs.aws.amazon.com/AmazonS3/latest/userguide/selecting-content-from-objects.html



A company uses Amazon Athena for one-time queries against data that is in Amazon S3. The company has several use cases. The company must implement permission controls to separate query processes and access to query history among users, teams, and applications that are in the same AWS account.
Which solution will meet these requirements?

  1. Create an S3 bucket for each use case. Create an S3 bucket policy that grants permissions to appropriate individual IAM users. Apply the S3 bucket policy to the S3 bucket.
  2. Create an Athena workgroup for each use case. Apply tags to the workgroup. Create an IAM policy that uses the tags to apply appropriate permissions to the workgroup.
  3. Create an IAM role for each use case. Assign appropriate permissions to the role for each use case. Associate the role with Athena.
  4. Create an AWS Glue Data Catalog resource policy that grants permissions to appropriate individual IAM users for each use case. Apply the resource policy to the specific tables that Athena uses.

Answer(s): B

Explanation:

The correct answer is
B: Here's a detailed justification:
Athena workgroups are designed to isolate queries and their history, providing a logical grouping for users, teams, or applications. Each workgroup has its own settings, including query result location, query metrics, and cost controls. Creating a workgroup for each use case directly addresses the requirement to separate query processes.
IAM policies can be used to control access to Athena workgroups based on tags. Tagging workgroups allows you to create granular permissions, ensuring that only authorized users or applications can access specific workgroups. This mechanism facilitates the separation of access based on use case, satisfying the permission control requirement. The condition elements in an IAM policy will use the tag keys to assign permissions to specific workgroups that the requesting principal has access to.
Option A is not ideal. Using S3 buckets to separate data can work, but it doesn't address the need to separate query processes or query history. Also, managing bucket policies for individual IAM users is cumbersome and doesn't scale well.
Option C is insufficient.
While IAM roles are important for granting Athena permissions, creating a separate role for each use case doesn't directly separate query processes and history within Athena itself. It may address general data access permissions but falls short of the fine-grained control needed for Athena-specific usage.
Option D might seem viable, but Glue Data Catalog resource policies mainly govern access to the metadata (tables, databases) rather than the query history and query execution context. It also does not separate query processes.
While important for data access, it doesn't fully address the problem of separating query processes.
Therefore, the best solution is to utilize Athena workgroups coupled with tag-based IAM policies to achieve the desired separation of query processes, query history, and access control for each use case.
Here are some links for further reading:
Amazon Athena Workgroups Identity-based policy examples for Amazon Athena AWS Resource Tags



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